---
type: "post"
id: "ai-scaling-vs-human-scaling-12-minute-read-2d8916"
slug: "ai-scaling-vs-human-scaling-12-minute-read-2d8916"
title: "AI Scaling vs Human Scaling (12 minute read)"
description: "**Thesis:** AI systems are improving at turning additional inference tokens into longer task horizons, but the evidence presented still shows weaker scaling than human performance with more time."
retrieval_nugget: "**Thesis:** AI systems are improving at turning additional inference tokens into longer task horizons, but the evidence presented still shows weaker scaling than human performance with more time."
published_at: "2026-10-12"
updated_at: "2026-10-12"
record_date: "2026-10-12"
date_kind: "published_at"
topics: []
source_urls: ["https://www.tobyord.com/writing/ai-scaling-vs-human-scaling"]
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editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# AI Scaling vs Human Scaling (12 minute read)

## Retrieval answer

**Thesis:** AI systems are improving at turning additional inference tokens into longer task horizons, but the evidence presented still shows weaker scaling than human performance with more time.

**Thesis:** AI systems are improving at turning additional inference tokens into longer task horizons, but the evidence presented still shows weaker scaling than human performance with more time.

Ord reports two concrete comparisons. In a METR comparison, humans gained about four times as much score per doubling of resources as the early AI systems tested. In the UK AI Security Institute chart, based on capture-the-flag hacking tasks, the estimated token increase needed for a tenfold improvement in the 80% time horizon fell from roughly 10,000× for earlier models to about 20× for models through May 2026.

**Mechanism:** A sub-unit slope on a log-log plot means token budget increases translate into less-than-proportional growth in estimated human-time task horizon; Ord says the fitted exponent improves across model releases. **Why now:** The newer AISI data indicates the scaling relationship may have improved substantially within less than a year, making extrapolations from early models unreliable. **First implication:** Teams should measure task success and horizon against inference budgets directly rather than assume more tokens substitute proportionally for human time. **Evidence boundary:** The essay relies on limited task suites and indirect comparisons, including early models in the METR comparison and internal hacking tasks in the AISI chart through May 2026; it does not establish a general capability law. **First watch condition:** A like-for-like study of recent models and humans across the same tasks and time budgets could show whether the improving exponent continues toward parity or plateaus.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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